Large Dynamic Networks and Patterns Visualization in NodeXL
Jacopo CirroneGraduate Student at University of Catania(Faculty of Computer Science Engineering)
Networks of different genres in the Real World
Social Transportation Biological, Chemical
Why Visualization is important?
Improving our understanding of networks
Networks sources
Network.txt
Network.dbNetwork.xml
Networks graphs
Improving our understanding of networks
Vizster [Heer 2006]
Infovis Co-authoring Network [Börner et al. 2004]
Clustering
Discovering the structure of the network
Visualization of Networks that evolve over time
Whitfield et al, J of. MBC 2002
Overview
• Introduction
• Large temporal networks Visualization in NodeXL
• Significant Anomalies Visualization in NodeXL
• Demonstration
• Conclusion and plan
ObamaCare Twitter Network
New Importer for Dynamic Network
Time
Dynamic Networks Visualization
Overview
• Introduction
• Large temporal networks Visualization
• Significant Anomalies Visualization
• Demonstration
• Conclusion and plan
Significant Anomalous Patterns Visualization
o Important Definition:o Pattern: Connected region of the graph that spans a certain
time interval with score higher than a given thresholdo For instance:
o Highway Network: low average speed on congested regions
Traffic Reported Accidents
Others Anomalous Patterns Examples
o Biology: Most essential pathways in a cell cyclephase? Activation patterns?
o Smart Grid: Energy consumption patterns for better planning of generation, storage and transportation.
Load Anomalous Patterns (SigSpot)
Reported Accidents
PATTERNS
Black = Overlapthose edges or nodes belonging to two or more different patterns in the given time interval Grey = No Patterns
Pattern
Pattern
Pattern
Overview
• Introduction
• Large temporal networks Visualization
• Significant Anomalies Visualization
• Demonstration
• Business logic Explanation
• Conclusion and plan
Overview
• Introduction
• Large temporal networks Visualization
• Significant Anomalies Visualization
• Demonstration
• Business logic Explanation
• Conclusion and plan
Behind the Visualization
o Let’s suppose we have:o All the Info about the Dynamic Network and the
Patterns in a text filePROBLEM:
HOW TO LOAD THOSE INFO IN ORDER TO GET AND VISUALIZE THEM IN A VERY FAST WAY?
Behind the visualization – Solution A
HOW TO LOAD THE Network And Patterns INFOS IN ORDER TO GET AND VISUALIZE THEM IN A VERY FAST WAY?
PROBLEM:
This Solution is not efficient for large networks
Behind the Visualization – Solution B
Network.db or
Patterns.db
Berkeley Database
HOW TO LOAD THE Network And Patterns INFO IN ORDER TO GET AND VISUALIZE THEM IN A VERY FAST WAY?
PROBLEM:
Berkeley Database
QUERY
Refresh Worksheet
Refresh Graph
Behind the Visualization – Solution B
Network-TREE BERKELEY DATABASE
[2,2][1,1] [4,4][3,3] [6,6][5,5] [8,8][7,7]
[1,8]
[1,4] [5,8]
[1,2] [3,4] [5,6] [7,8]
Array Sum [,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,]
Array Max [,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,]
Array Min [,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,]Array Avg [,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,]
Node or Edge AggregateNR NL
Generic NODE CONTENT
QUERY
6
1
2
3
4
5
AGGREGATE [4,6]
Overview
• Introduction
• Large temporal networks Visualization
• Significant Anomalies Visualization
• Demonstration
• Business logic Explanation
• Conclusion and plan
Conclusion
o This extension can be very useful for future researchers who are interested on:o Visualization of time evolving networkso Visualization of patterns within such networks
o We successfully managed networks witho Several thousands of nodeso Several thousands of edgeso Tens of thousands of time slices
Plan
o Extend the application to allow the user to import a network with different formats
o Extend the functionalities of patterns visualization to make the application more user-friendly:o User should detect immediately the edges or
nodes belonging to a certain patterno User should detect immediately the time interval
where a certain pattern is defined
Thanks!o Collaborators:
o Prof. Alfredo Ferro at Dept of Computer Science at Catania University
o Misael Mongiovi, Research Scientist at Dept of Computer Science UC Santa Barbara
o Prof. Ambuj K. Singh at Dept of Computer Science at UC Santa Barbara
Questions?
